Curve Fitting using Matlab

*********** Remarks of the Evaluator ************

Good Job

• You have not evaluated the goodness of fit
• There is no linear or cubic fit performed
• YOu have mererly reprodcued what was done in the video
• You havent answered the questions

Also, you have not estimated errors, in curve fitting, the error is estimated in two ways

• The sum of squares due to error (SSE)
• Root mean squared error (RMSE)

Also, blindly increasing the degree of the polynomial will not result in a better fit in all cases. The best way to proceed then is to divide the data points in subregions and curve fit for each region seperately.

NOTE : Also, try exploring on the fit( ) command and curve fitting toolbox in matlab if interested, this provides you with a plethora of options in optimizing a fit without using multiple equations and also calculates the fit characteristics for you !!!

SUGGESTION (GENERAL):

The other thing that you can do is to attach the code and images in

projects.skill-lync.com and paste the link over here.

If you have any doubts please ask us on WHATSAPP or raise a ticket on FRESHDESK or even call us to clarify all your doubts.

Please don't submit incomplete work as these challenges are made with an intuition of improving your understanding of the topic.

So, attempt all challenges sincerely.

Thank you.

*****************************************

clear allclose allclc%preparing the datacp_data = xlsread('curvefitting_data.xlsx','sheet1','A1:B700');temperature = cp_data(:,1);cp=cp_data(:,2);%plotting the dataplot(temperature,cp,'color','b','linewidth',2)xlabel('Temperature[K]');ylabel('Specific Heat[KJ/Kmol-K]');%curve fit data capturing%cp=a*T^2+b*T+c;coeffs=polyfit(temperature,cp,2);predicted_cp=polyval(coeffs,temperature);hold on%plotting the best curve fitting the dataplot(temperature,predicted_cp,'color','r','linewidth',2)legend('Original dataset','curvefit')

`300 940.3659512 301 940.5391218 302 940.7138188 303 940.8900327 304 941.0677546 305 941.2469751 306 941.4276852 307 941.6098757 308 941.7935375 309 941.9786616 310 942.1652389 311 942.3532602 312 942.5427167 313 942.7335993 314 942.925899 315 943.1196068 316 943.3147139 317 943.5112113 318 943.7090901 319 943.9083414 320 944.1089563 321 944.3109261 322 944.5142419 323 944.7188949 324 944.9248764 325 945.1321775 326 945.3407896 327 945.5507039 328 945.7619118 329 945.9744046 330 946.1881736 331 946.4032102 332 946.6195058 333 946.8370518 334 947.0558396 335 947.2758607 336 947.4971066 337 947.7195687 338 947.9432386 339 948.1681078 340 948.3941679 341 948.6214104 342 948.8498269 343 949.0794092 344 949.3101487 345 949.5420371 346 949.7750662 347 950.0092277 348 950.2445132 349 950.4809145 350 950.7184234 351 950.9570317 352 951.1967311 353 951.4375135 354 951.6793707 355 951.9222947 356 952.1662772 357 952.4113102 358 952.6573857 359 952.9044955 360 953.1526317 361 953.4017862 362 953.6519511 363 953.9031183 364 954.15528 365 954.4084283 366 954.6625551 367 954.9176526 368 955.1737131 369 955.4307285 370 955.6886911 371 955.9475932 372 956.2074268 373 956.4681843 374 956.729858 375 956.99244 376 957.2559227 377 957.5202985 378 957.7855596 379 958.0516984 380 958.3187074 381 958.5865789 382 958.8553054 383 959.1248792 384 959.3952929 385 959.666539 386 959.93861 387 960.2114983 388 960.4851966 389 960.7596974 390 961.0349933 391 961.3110769 392 961.5879409 393 961.8655779 394 962.1439806 395 962.4231417 396 962.7030539 397 962.9837099 398 963.2651025 399 963.5472245 400 963.8300686 401 964.1136278 402 964.3978948 403 964.6828624 404 964.9685237 405 965.2548714 406 965.5418986 407 965.8295981 408 966.1179629 409 966.406986 410 966.6966604 411 966.9869791 412 967.2779353 413 967.5695219 414 967.861732 415 968.1545588 416 968.4479953 417 968.7420348 418 969.0366704 419 969.3318953 420 969.6277027 421 969.9240859 422 970.221038 423 970.5185525 424 970.8166225 425 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990.7578232 489 991.0786189 490 991.3995735 491 991.7206817 492 992.0419382 493 992.3633378 494 992.6848752 495 993.0065452 496 993.3283425 497 993.650262 498 993.9722985 499 994.2944468 500 994.6167019 501 994.9390585 502 995.2615117 503 995.5840563 504 995.9066874 505 996.2293998 506 996.5521885 507 996.8750487 508 997.1979753 509 997.5209634 510 997.844008 511 998.1671043 512 998.4902474 513 998.8134324 514 999.1366545 515 999.4599088 516 999.7831906 517 1000.106495 518 1000.429818 519 1000.753153 520 1001.076497 521 1001.399845 522 1001.723192 523 1002.046533 524 1002.369865 525 1002.693181 526 1003.016478 527 1003.339751 528 1003.662996 529 1003.986208 530 1004.309382 531 1004.632514 532 1004.955599 533 1005.278634 534 1005.601613 535 1005.924533 536 1006.247388 537 1006.570175 538 1006.892889 539 1007.215526 540 1007.538081 541 1007.860551 542 1008.18293 543 1008.505216 544 1008.827403 545 1009.149487 546 1009.471464 547 1009.79333 548 1010.115082 549 1010.436714 550 1010.758222 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